The impact of COVID-19 on the diffusion of machine learning amongst rural farms: A study of Algeria, Egypt, Morocco, Tunisia and the United Arab Emirates

This study investigates the impact of the COVID-19 pandemic on the diffusion of Machine Learning (ML) among rural farms in Algeria, Egypt, Morocco, Tunisia, and the United Arab Emirates (UAE). Despite the proven benefits of ML in agricultural operations, rural farms in the MENA region have lagged in adoption due to barriers such as infrastructure deficits, costs, cultural factors, and limited knowledge/training. Through semi-structured interviews with 50 rural farm owners, this research explores shifts in attitudes towards ML adoption before and after the pandemic. The findings reveal a significant post-pandemic increase in ML awareness, confidence in technology usage, and recognition of ML's benefits, such as operational efficiency and cost reduction. Cultural resistance and knowledge gaps, once major barriers, have diminished, while infrastructure limitations and costs persist. The study introduces an empirically informed Machine Learning Adoption Framework version 3 (MLAFv3), highlighting changes in drivers and barriers pre- and post-COVID-19. It further proposes a practical ML-integrated crop management system to facilitate adoption among rural farmers. The findings contribute to addressing the digital divide in rural MENA agriculture and offer policy and practical recommendations to enhance ML adoption for rural economic resilience and cultural preservation.